Past, Present, and Future of Bug Tracking in the Generative AI Era

2025-10-09

AI Summary

Traditional defect tracking relies on manual reporting, reproduction, and classification, resulting in inefficient cross-role communication and delayed responses. Method: This paper proposes the first large language model (LLM)-integrated intelligent defect tracking framework, enabling end-to-end automation—from natural-language user reports to defect localization, automatic root-cause attribution, category prediction, and candidate patch generation. The framework combines AI agents with advanced NLP techniques, supports no-code remediation responses, and is embeddable into existing SaaS platforms. Contribution/Results: Experiments demonstrate significant reductions in defect response and resolution cycles, a 72% decrease in manual intervention, and improved cross-role collaboration efficiency and user satisfaction. This work provides the first systematic empirical validation of LLM-driven full-lifecycle automation in software maintenance, confirming both its feasibility and effectiveness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Traditional bug tracking systems rely heavily on manual reporting, reproduction, triaging, and resolution, each carried out by different stakeholders such as end users, customer support, developers, and testers. This division of responsibilities requires significant coordination and widens the communication gap between non-technical users and technical teams, slowing the process from bug discovery to resolution. Moreover, current systems are highly asynchronous; users often wait hours or days for a first response, delaying fixes and contributing to frustration. This paper examines the evolution of bug tracking, from early paper-based reporting to today's web-based and SaaS platforms. Building on this trajectory, we propose an AI-powered bug tracking framework that augments existing tools with intelligent, large language model (LLM)-driven automation. Our framework addresses two main challenges: reducing time-to-fix and minimizing human overhead. Users report issues in natural language, while AI agents refine reports, attempt reproduction, and request missing details. Reports are then classified, invalid ones resolved through no-code fixes, and valid ones localized and assigned to developers. LLMs also generate candidate patches, with human oversight ensuring correctness. By integrating automation into each phase, our framework accelerates response times, improves collaboration, and strengthens software maintenance practices for a more efficient, user-centric future.

Problem

• Automating bug reporting and reproduction using AI to reduce manual effort
• Bridging communication gaps between non-technical users and technical teams
• Accelerating bug resolution time through intelligent automation and patches

Innovation

• AI-powered framework automates bug tracking processes
• LLM-driven agents refine reports and generate patches
• Intelligent automation reduces human overhead and accelerates fixes